Indoor Localization Algorithm Based on Particle Filter Optimization in NLOS Environment

2019 ◽  
pp. 1143-1148
Author(s):  
Weiwei Liu ◽  
Tingting Liu ◽  
Lei Tang
Keyword(s):  
Particle Filter ◽  
Indoor Localization ◽  
Localization Algorithm

scholarly journals Vision-based Indoor Localization Algorithm using Improved ResNet

2020 ◽  
Vol 11 (2) ◽  
Author(s):  
Zeyad Farisi ◽  
Tian Lianfang ◽  
Li Xiangyang ◽  
Zhu Bin
Keyword(s):  
Indoor Localization ◽  
Localization Algorithm

Precise outdoor localization with a GPS–INS integration system

Robotica ◽  
2012 ◽  
Vol 31 (3) ◽  
pp. 371-379 ◽  
Author(s):  
Wonkyo Seo ◽  
Seoyoung Hwang ◽  
Jaehyun Park ◽  
Jang-Myung Lee
Keyword(s):  
Particle Filter ◽  
Discrete Wavelet ◽  
Signal Distortion ◽  
Localization Algorithm ◽  
Threshold Method ◽  
Variable Threshold ◽  
Unscented Particle Filter ◽  
Low Pass ◽  
Outdoor Localization ◽  
High Pass

SUMMARYThis paper proposes a precise outdoor localization algorithm with the integration of Global Positioning System (GPS) and Inertial Navigation System (INS). To achieve precise outdoor localization, two schemes are recently proposed, which consist of de-noising the INS signals and fusing the GPS and INS data. To reduce the noise from the internal INS sensors, the discrete wavelet transform and variable threshold method are utilized, and to fuse the GPS and INS data while filtering out the noise caused by the acceleration, deceleration, and unexpected slips, the Unscented Particle Filter (UPF) is adopted. Conventional de-noising methods mainly employ a combination of low-pass and high-pass filters, which results in signal distortion. This newly proposed system also utilizes the vibration information of the actuator according to the fluctuations of the velocity to minimize the signal distortion. The UPF resolves the nonlinearities of the actuator and non-normal distributions of the noise more effectively than the conventional particle filter (PF) or Extended Kalman Filter–PF. The superiority of the proposed algorithm was verified through experiments, and the results are reported.

MAPS: Indoor Localization Algorithm Based on Multiple AP Selection

2020 ◽  
Author(s):  
Pengyu Huang ◽  
Haojie Zhao ◽  
Wei Liu ◽  
Dingde Jiang
Keyword(s):  
Indoor Localization ◽  
Localization Algorithm

scholarly journals Indoor Localization Based on VIO System and Three-Dimensional Map Matching

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2790 ◽  
Author(s):  
Jitong Zhang ◽  
Mingrong Ren ◽  
Pu Wang ◽  
Juan Meng ◽  
Yuman Mu
Keyword(s):  
Random Field ◽  
Indoor Localization ◽  
Conditional Random Field ◽  
A Priori ◽  
Three Dimensional ◽  
Map Matching ◽  
Localization Algorithm ◽  
Position Information ◽  
Feedback Information ◽  
Poor Lighting

High-precision indoor localization plays a vital role in various places. In recent years, visual inertial odometry (VIO) system has achieved outstanding progress in the field of indoor localization. However, it is easily affected by poor lighting and featureless environments. For this problem, we propose an indoor localization algorithm based on VIO system and three-dimensional (3D) map matching. The 3D map matching is to add height matching on the basis of previous two-dimensional (2D) matching so that the algorithm has more universal applicability. Firstly, the conditional random field model is established. Secondly, an indoor three-dimensional digital map is used as a priori information. Thirdly, the pose and position information output by the VIO system are used as the observation information of the conditional random field (CRF). Finally, the optimal states sequence is obtained and employed as the feedback information to correct the trajectory of VIO system. Experimental results show that our algorithm can effectively improve the positioning accuracy of VIO system in the indoor area of poor lighting and featureless.

Single Beacon Indoor Localization System Based on Counter-Synchronized Compass and RSSI

2014 ◽  
Vol 627 ◽  
pp. 217-222
Author(s):  
Kok Seng Eu ◽  
Kian Meng Yap ◽  
Tiam Hee Tee
Keyword(s):  
Indoor Localization ◽  
Target Item ◽  
Optimal Solution ◽  
Research Area ◽  
Target Object ◽  
Localization Algorithm ◽  
Distance Information ◽  
Localization Method ◽  
Localization System ◽  
Lost And Found

Indoor localization system has been a popular research area in recent decades and many of them are based on multiple beacons localization method. However, there are some special applications to which the multiple beacons method is not an optimal solution due to its overdesign and cost of redundancy. Multiple beacons method uses at least three transducers and each transducer’s location must be known to find the location of a target object by using either Triangulation or Trilateration calculation. When the multiple beacons method is applied in an items lost and found system, the precise Cartesian coordinates of a target item can be found, but it is definitely overdesign and incurring redundant cost. It is due to the fact that the target item requires only two simple information i.e. Clock orientation and distance information; therefore, single beacon is enough for the task. In this paper, we propose a single beacon localization method to optimize the solution in the items lost and found system by utilizing clock orientation and estimated distance information. The proposed single beacon localization algorithm has been demonstrated and proven that it can be one of the optimal solutions for items lost and found system.

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